[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126889-en":3,"doc-seo-126889-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126889,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Designing complex concentrated alloys with quantum machine learning and language modeling","Designing novel complex concentrated alloys (CCAs) is challenging because component–property relationships are high-dimensional and hard to tune through experience or limited empirical rules. This study uses quantum computing together with machine learning as a proof of concept for physical metallurgy. A quantum support vector machine with fine-tuned quantum kernels predicts single-phase CCAs with up to 89.4% accuracy. It also identifies 1,741 lightweight candidates via a new text-mining screening method, while introducing a controllable evaluation of data noise impact on model performance.","ll   \nArticle  \nDesigning complex concentrated alloys with quantum machine learning and language modeling  \n\n|  |  |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n\nWe combine two different types of machine learning models to design complex concentrated alloys. Our target is to predict single-phase solid solutions. The quantum machine learning model predicts the likeliness of an alloy as a solid solution. Then, the language model provides input to calculate the ‘‘context similarity’’ of chemical elements for further screening. Eventually, our work provides a list of alloy candidates based on a few criteria.  \nZongrui Pei, Yilun Gong, Xianglin Liu, Junqi Yin [peizongrui@gmail.com](peizongrui@gmail.com)  \nHighlights  \nWe designed about 1,700 complex concentrated alloys using two machine learning methods  \nWe provide a proof-of-concept application of quantum machine learning in alloy design  \nOur fine-tuned quantum machine learning model achieved an accuracy of 89.4%  \nWe devise an approach to study the effect of noise on quantum machine learning models  \nPei et al. , Matter 7, 1–14  \nOctober 2, 2024 ª 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.  \n[https://doi.org/10.1016/j.matt.2024.05.035](https://doi.org/10.1016/j.matt.2024.05.035)  \nPlease cite this article in press as: Pei et al., Designing complex concentrated alloys with quantum machine learning and language modeling, Matter (2024), [https://doi.org/10.1016/j.matt.2024.05.035](https://doi.org/10.1016/j.matt.2024.05.035)  \nll   \nArticle  \nDesigning complex concentrated alloys with quantum machine learning and language modeling  \nZongrui Pei, 1,6,* Yilun Gong,2,3 Xianglin Liu,4 and Junqi Yin5  \nSUMMARY  \nDesigning novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated highdimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4% . The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. This study provides a practical and general approach to designing CCAs based on quantum technologies.  \nINTRODUCTION  \nHumans have thousands of years of history in designing alloys that have played critical roles in our civilization, represented by bronze, steel, and many others. Nowadays, alloys have become increasingly complicated in components, phases, and microstructure, making it challenging to tune material properties, depending on researchers’ experience and trial-and-error strategy. This trend is evidenced by the appearance of complex concentrated alloys (CCAs) that discard the concept of base elements and usually have four or more principal elements.1 ,2 CCAs have been found with exceptional mechanical properties,3–6 corrosion resistance,7 functional properties,8 etc. In particular, the exceptional mechanical properties can be associated with the unique deformation behavior9–12 in the materials. Fortunately, machine learning (ML) techniques have greatly succeeded in tackling the complex structure (component) and property relationship.13–16 The success of classical ML models is mainly due to their ability to describe complicated nonlinear problems after learning from existing data.16  \nML methods are usually data hungry and computationally expensive, while quantum co","cbCaihFU6WDGP37R","https://ap.wps.com/l/cbCaihFU6WDGP37R","pdf",9502129,1,15,"English","en",105,"# SUMMARY\n# INTRODUCTION\n# PROGRESS AND POTENTIAL","[{\"question\":\"该研究的目标是什么？\",\"answer\":\"研究旨在设计复杂浓缩合金（CCAs），并预测单相固溶体的可能性。\"},{\"question\":\"量子机器学习与语言模型分别用于什么环节？\",\"answer\":\"量子机器学习模型用于预测合金作为单相固溶体的可能性；语言模型用于计算化学元素之间的“语境相似度”，从而进行进一步筛选。\"},{\"question\":\"研究如何评估噪声对模型性能的影响？\",\"answer\":\"提出一种可控的方法来评估数据噪声对量子机器学习模型表现的影响，并发现要获得高性能量子支持向量机模型需尽量降低噪声水平。\"}]","Designing complex concentrated alloys with quantum machine learning and language modeling | PDF",1785935443,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"designing-complex-concentrated-alloys-with-quantum-machine-learning-and-language-modeling","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/designing-complex-concentrated-alloys-with-quantum-machine-learning-and-language-modeling/126889/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"该研究的目标是什么？","Question",{"text":75,"@type":76},"研究旨在设计复杂浓缩合金（CCAs），并预测单相固溶体的可能性。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"量子机器学习与语言模型分别用于什么环节？",{"text":80,"@type":76},"量子机器学习模型用于预测合金作为单相固溶体的可能性；语言模型用于计算化学元素之间的“语境相似度”，从而进行进一步筛选。",{"name":82,"@type":73,"acceptedAnswer":83},"研究如何评估噪声对模型性能的影响？",{"text":84,"@type":76},"提出一种可控的方法来评估数据噪声对量子机器学习模型表现的影响，并发现要获得高性能量子支持向量机模型需尽量降低噪声水平。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]